Keywords

Abstract

The increasing availability of location-acquisition technologies (such
as GPS and GSM networks) and mobile computing techniques
has generated a lot of spatial-temporal trajectory data and indi-
cates the mobility of diversiﬁed moving objects such as people,
vehicles, and animals. This brings new opportunities to identify
abnormal activities of moving objects. This paper describes our
detection of anomalies in human trajectory data using a hybrid
grid-based hierarchical clustering method based on Hausdorﬀ dis-
tance, which is suitable for measuring the similarity between trajec-
tories of diﬀerent lengths. The trajectories were ﬁrst transformed
into grid-based trajectories using a grid structure. After that,
the grid-based trajectories were clustered based on their pairwise
Hausdorﬀ distances by applying diﬀerent versions of hierarchical
clustering algorithms. We evaluated our research result using a real-
life dataset (published by Microsoft Research Asia), ground truth
reconstructed by us, and evaluation criteria widely used in data
mining. The experimental results demonstrate that the proposed
algorithm is more eﬀective and much faster than the traditional
hierarchical clustering algorithm according to the pairwise comparison results.